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BU Law students! Apply by April 5 for the 2019-2020 clinic!

By Technology Law ClinicMarch 25th, 2019

Attention BU Law students! Applications are open to rising 2Ls and 3Ls to join the Technology Law Clinic for the 2019–2020 academic year. We're looking for all students with an interest in technology legal issues, be that in data privacy, intellectual property, cybersecurity, or media and First Amendment law.

No prior background in technology is required, but the clinic has a course co-requisite requirement: students must have taken or take a class in the areas of intellectual property, privacy, or cybersecurity. A number of courses meet this criteria.

The deadline to apply for the clinics is 5pm on April 5. Full application instructions are available here.

We hope that you'll join!

Understanding and avoiding risks associated with machine learning

By Technology Law ClinicFebruary 13th, 2019

Written by Imran Malek, Technology Law Clinic

Introduction

Nearly every aspect of our lives is either enhanced by or depends on computing technology – whether it be through the networks and platform that we access using the devices on our desktops, under our TVs, or in our pockets. With the advent of machine learning, the technology that essentially allows to make informed decisions using up to date data with minimal user input, and the “perfect storm” of ubiquitous connected computing devices and affordable data processing mechanisms, we are now at a place where the more we use our technology, the better (or at least more tailored) our experiences can be. machine learning plays a big part in most of our lives. Among many other things, technology that uses machine learning powers our spam filters, optimizes inventory to prevent food waste due to spoilage in grocery stores, and even curates our digital radio stations to the point where never feel the need to skip a track.

With machine learning’s ubiquity, however, come pitfalls. Although implemented with the best of intentions, machine learning has in some areas reinforced biases, stifled opportunities, or produced wholly negative outcomes:[1]

The benefits—and these specific drawbacks—of machine learning also point to another area of concern: personal data privacy. As more and more of our lives are lived online, we produce more data. This increase in production has led to a data “gold rush” where businesses compete to collect the most data possible so that they can make the most informed decisions. With such significant financial implications come increased scrutiny to the data that we produce or is produced on our behalf. In the context of innovative applications of machine learning where personal data is often a critical part of the algorithmic decision-making process, legislation like the European Union’s General Data Protection Regulation (“GDPR”) provides rights to  end users, which could lead to serious consequences for a startup that doesn’t respect those rights. With these drawbacks also come legal challenges that a startup implementing may face, including challenges arising from the unauthorized use of data to “train” machine learning algorithms, algorithmic bias and anti-discrimination law, and collecting, processing, and transmitting data at the expense of user privacy.

With all that in mind, these posts will dive into machine learning and elaborate on the legal issues surrounding it through three major topics:

  1. An introduction to machine learning
  2. How startups can source the right data for a machine learning algorithm while maintaining user privacy, especially in the context of relevant laws like the GDPR.
  3. Implementing machine learning models live while ensuring transparency and promoting fairness in decision making

An Introduction to Machine Learning

While there are plenty of online tutorials (including one of the first and most well-known Massive Online Open Courses) that discuss the details, techniques, and nuances associated with machine learning (concepts like “supervised learning” or “naive bayes classification,” for example) for this post and for the other posts in this series we will simplify machine learning with a definition paraphrased from Sujit Pal, and Antonio Gulli:

Machine learning focuses on teaching computers how to learn from and make predictions based on data.

The data that feeds those predictions, especially today, usually encompass what the business and technology worlds have labeled “big data.” Without going into too much detail, we can define big data and its machine learning application through “three Vs”:

  1. Volume: The amount of data out there is vast, and continues to expand every second.
  2. Variety: As more and more devices, applications, and services are brought online, data is efficiently stored in ways that are easily accessible and usable.
  3. Velocity: Data is now collected in real time, and real time collection, when paired with cheap computing power, means that data can instantly be used.

The scale and speed at which data is generated, classified, and stored make it impossible for humans alone to analyze, interpret, and execute on that data. Accordingly, machine learning has become one of the tools that businesses, governments, researchers, and even individuals use to influence or even make decisions.

The actual mechanism of turning data into a decision is usually referred in the context of “models.” These models represent collections of patterns that, when combined, produce generalizable trends that empower decision making. To put it simply, models are generalized mathematical representations of real-world processes such that when data is input into a model the result would mirror the real-world output.[2]  Since processes in the real world are often the result of multiple independent variables, models need to be “trained” in order to be accurate. It is through this training that the “learning” behind machine learning becomes evident.

To understand training, and machine learning in general, let’s think through a simple example where I want to build a tool that helps me make a decision that many of us face every day: what to order at a coffee shop. In this simple example, I want my model to take in three variables and then use them to decide what I drink I should buy:

Variable

Possible Values

Time of Day

Morning, Afternoon, Evening

Temperature Outside

Hot, Cold

Day of Week

Monday, Tuesday, Wednesday, Thursday,
Friday, Saturday, Sunday

Right now, without any training, my model doesn’t know what to do, it doesn’t know what to do, so I need to train it. To do so, I’ll start by keeping track of one week’s worth of my typical drink purchases:

Day of Week

Time of Day

Temperature Outside

Drink I Ordered

Monday

Morning

Cold

Hot Coffee

Tuesday

Morning

Hot

Iced Coffee

Wednesday

Morning

Cold

Hot Coffee

Thursday

Evening

Cold

Iced Herbal Tea

Friday

Afternoon

Hot

Hot Coffee

Saturday

Evening

Cold

Hot Herbal Tea

Sunday

Morning

Hot

Iced Coffee

Immediately, you can see that there’s a pattern forming – when it’s Hot outside, I prefer cold drinks, and when it’s Cold outside, I prefer hot drinks. You can also see that when it’s in the Evening, I prefer to drink tea instead of coffee.

When the new week starts and it’s Monday, I load my now trained model into an application on my phone, go to my local coffee shop, and try to make a decision. I go in the Morning, and it happens to be Cold outside, so naturally, my model suggests that I should get a Hot Coffee. This is great! I don’t have to worry about making a decision. I

subsequently order my hot coffee and make sure to record that it was Morning, it was Cold outside, and I ordered a Hot Coffee on a Monday. I then input this data into my model. At this point, I’ve not only trained my model, I’ve also provided feedback using to it help validate it.

If we were making a “production ready” machine learning model (for example, one that might be built into a coffee chain’s mobile app), there would be many more variables factored in to the algorithm, including: weather on the current day, what people around me have ordered, what month it is, whether or not I ordered food, what coffee shop location I happen to be visiting, what genre of music is playing in the coffee shop, and many, many, many more!

Now, let’s take it one step further and talk about what might happen if there’s no training data available – on the next day, a Tuesday morning, I skip my morning cup of coffee and decide to walk in to the shop on a Cold Afternoon. My model is left with a dilemma – there’s no training data for this exact combination of variables. Does it suggest an Iced Coffee, because that’s what I ordered on a Tuesday? Or does it suggest a Hot Coffee, because that’s what I ordered the last time I went in to my coffee shop in the Afternoon? While a human might intuitively think that temperature is a more important factor than day of the week when it comes to making a beverage decision, my model doesn’t know that, so it randomly picks from the two likely options and suggests an Iced Coffee. Scoffing at the prospect of drinking something cold on a cold day, I reject that decision, input my rejection, and order a Hot Coffee. My model now knows that on Cold Tuesday Afternoons, I drink Hot Coffee. It also has learned, based off of my decision, that when I make a decision that I haven’t made before, I weigh the temperature as more decisive factor than the day of week.

In the real world, the data used from training can come from a variety of sources, but that data most often comes from historical data that was collected, analyzed, and processed by humans. Unfortunately, since that data is derived from human behavior, historical data may also reinforce preexisting biases (like we saw earlier with the concerns around predictive policing algorithms).

Conclusion

In the next post of this series, we’ll touch on the relevant laws that touch algorithmic bias, and we’ll also provide recommendations to organizations using machine learning on how to diagnose and overcome bias in the tools and technologies that they build.

More

BU Law students: come work at the BU/MIT clinics this summer!

By David GrossJanuary 23rd, 2019

Attention BU Law students! We are happy to announce that applications for the 2019 summer fellowships with the two BU/MIT clinics are now available on Symplicity. We'll be hosting an information session on Monday, February 4 at 1pm in Room 209 to discuss these opportunities. The deadline to submit is 4pm on February 8.

The Technology Law Clinic is a pro bono service for students at MIT and BU who seek legal assistance with their innovation-related academic and extracurricular activities. Fellows will represent students on legal matters related to technology research and innovation, including in the areas of intellectual property, data privacy, cybersecurity, and media law. Clients work in fields such as artificial intelligence, Internet platforms, drones and robotics, and novel forms of technology research, ventures, and advocacy.

Fellows in the Startup Law Clinic will provide strategic legal and business advice to startups, assisting student entrepreneurs in the MIT and BU communities with the corporate, transactional, and intellectual property issues that arise in the process of turning their ideas into operating businesses.  Working under the guidance of the clinic directors, the Fellows will manage each step of the client relationship, from the initial intake interview through the completion of the engagement.

We are honored to announce that thanks to a generous contribution by Antonio Gomes (BUSL '96), both the Technology Law Clinic and the Startup Law Clinic will be hiring two students as Matthew Z. Gomes Fellows.

The Mathew Z. Gomes Fellowship, established in memory of Mr. Gomes’s son, is specifically open to students who: 

  • have demonstrated experience in or commitment to working with historically underserved or underprivileged populations;
  • are the first generation in one’s family to attend law school;
  • are socioeconomically disadvantaged; and/or
  • have overcome substantial educational or economic obstacles or personal adversity.

In addition, the Technology Law Clinic will be hiring an additional Summer Fellow, for a total of three summer opportunities. The Startup Law Clinic will be hiring three additional Summer Fellows, for a total of five summer opportunities.

Working under the guidance of their respective clinic's faculty, these fellows will manage the work of their clinic’s clients over the summer. Total compensation for the 10-week program will be $6,000.00 per fellow.

Rising 2L and 3L students are encouraged to apply. Applicants should submit a cover letter, resume, and transcript via Symplicity by 4pm on February 8, 2019. If applying for the Matthew Z. Gomes Fellowship, the cover letter should address the fellowship's specific qualifications under that fellowship.

Using the BU Law Symplicity portal you can find the listings at:

  • Matthew Z. Gomes Fellow, Technology Law Clinic – listing 30445
  • Summer Fellow, Technology Law Clinic – listing 30444
  • Matthew Z. Gomes Fellow, Startup Law Clinic – listing 30443
  • Summer Fellow, Startup Law Clinic – listing 30442

Please contact Andy Sellars or Jim Wheaton if you have questions about these opportunities.

Protecting Access to Government Databases under Public Records Law

By Andrew F. SellarsJanuary 11th, 2019
Technology Law Clinic students Patrick Wilson (BUSL '20), Ally Faustin (BUSL '20), Zach Sisko (BUSL '19), and Lyndsey Wajert (BUSL '19), outside of the John Adams Courthouse after the oral argument.

On Thursday, the Supreme Judicial Court of Massachusetts heard arguments in the case Boston Globe Media Partners, LLC v. Department of Public Health. The Clinic filed an amicus curiae brief (PDF) in the case on behalf of the editorial staff of The Tech, the MIT student newspaper, together with the Reporters Committee for Freedom of the Press, Metro Corp. (publisher of Boston magazine), the New England Center for Investigative ReportingNew England First Amendment Coalition, the New England Newspaper and Press Association, the New York Times Company, North of Boston Media Group (publisher of several regional newspapers in northeastern Massachusetts and southern New Hampshire), and the editorial staff of the Free Press, the newspaper of the University of Southern Maine. 

The case concerns a request that the Boston Globe made under the Massachusetts Public Records Law (PRL) for an electronic copy of two databases containing Massachusetts birth and marriage records, held by the Department of Public Health. The records in question contain only basic information related to births and marriages, and are already made available to the public on an individual level. The Department denied the request, arguing among other things that the disclosure of the entire set of records would be an unwarranted invasion of privacy, and thus is exempt under specific provisions in the PRL.

The amicus brief, filed in support of the Boston Globe, brings a combination of law, data science, and examples from journalism to show why disclosure of this set of records does not create any privacy harms. Quoting from the brief:

This Court should not abandon its well-settled framework for evaluating exemption requests predicated on privacy concerns under the PRL simply because the Records contain numerous entries. To the extent that large datasets are different than other records, they can be analyzed simply by distinguishing between the “breadth” and the “depth” of the dataset in question. “Broad” but “shallow” datasets, like the Records here, which relate to numerous individuals but contain few details, pose much lower privacy risks than “deep” datasets of any breadth, which contain detailed information about each person in the set. […] In this case, because these Records contain very little information about each person, disclosing the Records will not create any new privacy risks.

The brief also gives a variety of examples of where journalists used records like these on stories about government accountability and other issues of public concern.

"The editors at The Tech were happy to participate in this amicus brief," said Emma Bingham, Editor in Chief of The Tech. "As both journalists and students at one of the world’s top technical institutes, we understand the value of data access. We hope the Department of Public Health datasets in question in this case will soon be available to all newspapers, so that they use them to continue to hold our government accountable and produce interesting and informative reporting. We believe this is especially important for small local and university newspapers, which play an important role in their communities, but often have fewer resources to spend gathering data."

Clinic students Alexandra Faustin (BUSL '20), Zachary Sisko (BUSL '19), Lyndsey Wajert (BUSL '19), and Patrick Wilson (BUSL '20) drafted the brief, with help from Clinic director Andy Sellars, Visiting Clinical Assistant Professor Julissa Milligan, and attorneys at the Reporters Committee for Freedom of the Press.

We are now the Technology Law Clinic

By David GrossJanuary 3rd, 2019
Andy Sellars, director of the Technology Law Clinic, and Jim Wheaton, director of the Startup Law clinic, at the BU/MIT Clinics Celebration and Reunion on November 2, 2018.
Andy Sellars (left), director of the Technology Law Clinic, and Jim Wheaton, director of the Startup Law Clinic, at the BU/MIT Clinics Celebration and Reunion on November 2, 2018.

We're starting 2019 off with a change to our name! As of January 1, we are now officially the BU/MIT Technology Law Clinic, instead of the Technology & Cyberlaw Clinic.

We announced this change on November 2, at a gathering of clinic alumni and community members. Our sister clinic is now named the BU/MIT Startup Law Clinic (instead of the Entrepreneurship and Intellectual Property Clinic). Both clinics remain under the Entrepreneurship, Intellectual Property, and Cyberlaw Program at BU Law.

We found our new names to be a natural outgrowth of our first few years of operation, as they tend to be the way in which each clinic is understood by students at BU and MIT — our clinic focusing on all technology-related legal issues, and our colleagues focusing on business startup issues. Both clinics bring high-quality representation on matters of intellectual property law, with our clinic focusing as well on data privacy and cybersecurity matters, and the Startup Law Clinic focusing on business and finance matters.

The Technology Law Clinic and the Startup Law Clinic both look forward to continuing to provide exceptional legal services to BU and MIT students on their research, projects, and ventures.

 

 

Panel discussion on “Digital Muckraking and Undercover Browsing,” October 18

By Andrew F. SellarsOctober 3rd, 2018

October 18, 12:30pm – 2pm
BU Law, Room 410

A panel discussion with faculty from BU School of Law, BU College of Communications, investigative journalists, and the Reporters Committee for Freedom of the Press.

A growing number of researchers, journalists, and investigators are developing tools and techniques to understand the way in which private technology companies regulate online content and commerce. These include web scrapers, puppet accounts, and numerous other data-gathering technologies. Use of these tools however, can present legal risks under state and federal anti-hacking laws, as well as other related legal issues. Join a panel of lawyers, journalists, and free press advocates, to discuss how this tension is playing out and what it means for one's right to research and right to know.

Panelists will include:

  • Andy Sellars, Director of the BU/MIT Technology & Cyberlaw Clinic, BU School of Law
  • Maggie Mulvihill, Associate Professor of the Practice of Computational Journalism, BU College of Communications
  • Gabe Rottman, Director of the Technology and Press Freedom Project, Reporter's Committee for Freedom of the Press
  • Saurabh Datar, Data Journalist, The Boston Globe
  • Isaiah Thompson, Investigative Reporter, WGBH

The event is free and lunch will be provided. To help us estimate food, please RSVP to Prof. Mulvihill at mmulvih@bu.edu no later than October 15.

Massachusetts Non-Competition Reform: Students Exempt Starting October 1

By Technology & Cyberlaw ClinicSeptember 26th, 2018

By Lauren Hoepfner 

Attention all 150,000+ students in Boston: starting October 1, short-term student employees can no longer be bound by noncompetition agreements after they leave a job.

Massachusetts recently passed a sweeping reform that prohibits the enforcement of noncompetition agreements, or “noncompetes,” against both undergraduate and graduate students. Under the new law, employers cannot enforce noncompetes against full or part-time students who are hired for an internship or other short-term employment, whether paid or unpaid.

For noncompetes to be enforceable against non-student employees and independent contractors, they must be reasonable in scope to protect a legitimate business interest as well as limited to one year and to the geographic area where the employee worked in the prior two years. The noncompete must be supported by a “garden leave” clause—an agreement to pay the employee during the restricted period—or another mutually-agreed benefit to the employee, and provided to the employee at least ten days before employment begins.

The new law goes into effect on October 1, 2018.

What is a noncompete?

A noncompete is an agreement between an employer and a current or prospective employee that prohibits the employee from working for a competitor after they leave their job. Noncompetes are often signed before an employee or independent contractor starts work—either as a stand-alone agreement or within an employment contract.

Employers use noncompetes to prevent former employees from disclosing valuable information or using inside knowledge to benefit a competitor. But Massachusetts has long recognized that noncompetes can be abused by employers to prevent their employees from jumping ship, rather than simply protecting the company’s information. To protect workers from this type of abuse and promote competition for talent, Massachusetts has only allowed employers to enforce a noncompete if it was “necessary to protect legitimate business interest, reasonably limited in time and space, and consonant with the public interest.” In short, noncompetes had to target a specific business interest rather than simply serving as an employee retention tool. For example, a noncompete that prohibited a former bank executive from soliciting the bank’s customers for a period of time may be valid, but an agreement prohibiting a hairstylist from working at a different salon would not be enforced.

Now there are even stronger limits.

What noncompetes are covered by the reform bill?

The reforms only apply to post-termination noncompetes, or agreements that restrict an employee’s choice of work after leaving the company. Concurrent noncompetes—agreements that prohibit employees from working for competitors while employed—remain valid and enforceable.

The law is not retroactive. The law exempts noncompetes signed before October 1, including those signed by interns, and these agreements may still be enforceable. Noncompetes signed before October 1 will still have to protect a legitimate business interest, be reasonably limited in time and space, and consonant with public policy to be enforceable. However, in light of the reform, there may be some appetite among employers to reevaluate and update existing noncompetes as a best practice.

What does the new law say?

            For students – 

Students cannot be bound by a noncompete, and any noncompete signed by an intern after October 1, 2018 will not be enforceable.

A noncompetition agreement shall not be enforceable against . . . undergraduate or graduate students that partake in an internship or otherwise enter a short-term employment relationship with an employer, whether paid or unpaid, while enrolled in a full-time or part-time undergraduate or graduate educational institution.

Who is a “student”? The law covers undergraduate and graduate students enrolled full-time or part-time in a college or university. It applies to paid and unpaid internships and short-term employment.

This means that students have the right to let their employers know that any noncompete provision included in an employment contract cannot be enforced against them. Savvy employers may look to other agreements (see below), to restrict student employees’ behavior, so students should look through all agreements carefully and seek legal advice if they have questions.

BU and MIT students, feel free to come and talk to us!

            For hourly employees – 

Employees who are “nonexempt” and overtime eligible under the Fair Labor Standards Act (FLSA) also cannot be bound by noncompetes under the new law. Nonexempt employees under the FLSA are typically compensated hourly rather than by salary, and do not have a guaranteed minimum compensation amount. Nonexempt employees also typically do not perform high-level executive, professional, or administrative work. The new law prohibits the enforcement of noncompetes against this category of employees.

Like students, hourly employees can push back against employers who include noncompete provisions in employment agreements. They should also pay close attention to the terms of other agreements (see below) that may restrict their behavior in other ways.

            For all employees – 

Noncompetes entered into on or after October 1 are only valid for one year after the end of employment, and they must be limited in scope. To be valid and enforceable, a noncompete must be in writing, signed by both the employer and employee, and provided to the employee at least 10 days before employment begins.

The agreement can be no broader than necessary to protect an employer’s “legitimate business interest.” Under the law, the noncompete must be targeted at protecting an employer’s trade secrets, confidential information that does not qualify as a trade secret, and/or the employer’s goodwill. For example, a noncompete agreement can be targeted at protecting business plans, but cannot be targeted at protecting the employer from competition generally. The employer must show that the interest can’t be protected by a less restrictive agreement like a nonsolicitation or a nondisclosure agreement.

It must be supported by a garden leave clause—an agreement to pay the employee during the restricted period—or another mutually-agreed benefit to the employee. Garden leave clauses must provide at least 50% of the employee’s highest salary in the 2 years preceding the employee’s termination. Other benefits could include stock options or a signing bonus.

A noncompete can’t last longer than 12 months, and can only apply to the geographic area in which the employee worked for the last 2 years. Finally, the agreement must be consonant with public policy. For example, courts have held that noncompetes signed by sandwich shop workers, hair dressers, or other blue-collar workers are against public policy because these low-wage workers would be severely limited in their post-termination employment opportunities.

What can employers do to protect confidential information?

Although the new law places limits on noncompetes, companies still have many ways to protect confidential and sensitive information provided to students and hourly employees. And of course, there are separate protections for a company’s trade secrets.

The law does not apply to other agreements that protect company information. Employers can still require employees, including student interns and hourly workers, to sign the following agreements:

  • Nondisclosure agreements (NDAs): NDAs promote confidentiality by preventing employees from disclosing proprietary information gained in the course of their employment.
  • Proprietary Information and Inventions Assignments (PIIAs): PIIAs ensure that all relevant intellectual property created or contributed to by an employee during the term of his employment remains the property of the employer.
  • Nonsolicitation agreements: These agreements may (a) prohibit employees from solicitating a company’s clients or customers for his or her own benefit or for the benefit of a competitor after leaving the company; (b) prohibit a former employee from poaching current employees; (c) prohibit a former employee from transacting business with the former employer’s customers, clients, or vendors.
  • Other agreements that the new law expressly does not restrict the use of are (a) noncompetes made in connection with the sale of a business entity; (b) noncompetes outside of an employment relationship (i.e. independent contractor agreements); (c) forfeiture agreements; (d) garden leave clauses; (e) noncompetes drafted at the cessation of employment; and (f) agreements where an employee agrees not to reapply for employment at the same employer after termination.

Employers will likely turn to these other employment agreements to fill gaps left by the absence of noncompetes, and as a result, make them more restrictive.

If you own or operate a business, the above-mentioned agreements can provide alternative ways to protect your company’s intellectual property and confidential information. But remember, these alternative agreements must be reasonable and targeted at protecting confidential information in order to be enforceable.

*   *   *

Lauren Hoepfner is a 3L at BU Law and the summer 2018 Matthew Z. Gomes Fellow with the Entrepreneurship & Intellectual Property Clinic. She is most interested in corporate governance and compliance. 

CIA ordered to produce additional records in clinic FOIA case

By Andrew F. SellarsSeptember 24th, 2018
Members of the TCLC clinic who worked on Dr. Johnson's case, outside of the Moakley Courthouse after oral argument. From left to right: Lindsay Ladner (BUSL '18), Katherine Welch (BUSL '18), Mandy Wang (BUSL '18), TCLC client Dr. Amy Johnson, Audra Sawyer (BUSL '18), and TCLC Director Andy Sellars

For the past two years, the Technology & Cyberlaw Clinic has represented Amy Johnson, a PhD student (now graduate) of MIT in a Freedom of Information Act case against the Central Intelligence Agency. Earlier this year the CIA asked the court to close the case, arguing that they had now finished processing this request and were under no obligation to produce specific parts of Dr. Johnson's request. In April, TCLC student Audra Sawyer (BUSL '18) argued in opposition to the CIA's motion, saying that the CIA's search was inadequate, aspects of her request had yet to be fulfilled, and the material that was redacted under FOIA should be disclosed.

We're happy to report that last week the United States District Court for the District of Massachusetts sided with our client, and ordered the CIA to produce additional records in response to Dr. Johnson's FOIA request, and un-redact some of the produced records.

The records sought in the FOIA request relate to the CIA’s Twitter account. The CIA launched this account in June 2014, and it is now one of the few outlets by which the CIA regularly communicates with the public. In an unusual move for a federal agency, the CIA frequently humor and sarcasm when communicating with the public. Critics and scholars have wondered why the CIA would decide to take this contentious and unconventional approach when communicating with the public online. Dr. Johnson's request sought records that would shed light on the decision to design the account in this way, how users are instructed to operate the account, and the applications that the CIA uses to manage its account.

Dr. Johnson was one of the first clients of the clinic, and numerous TCLC students have worked with her on this case over the past two years. Clinic students Gabriella Andriulli (BUSL '17), Tavish Brown (BUSL '17), and Wes Howe (BUSL '17) from the clinic's inaugural class worked on the pre-litigation negotiations and complaint in the case. Summer 2017 students Courtney Merrill (BUSL '19) and Luke Sutherland (BUSL '19) worked on several of the early-stage litigation matters. Students Audra Sawyer (BUSL '18), Darija Micovic (BUSL '18), Katherine Welch (BUSL '18), Lindsay Ladner (BUSL '18), and Mandy Wang (BUSL '18) from the clinic's 2017–18 class worked on the summary judgment briefing and oral argument. Summer 2018 students Rachael Walker (BUSL '20), Patrick Wilson (BUSL '20), and Sally Gu (BUSL '20) worked on some additional briefing that was raised after oral argument. Current TCLC students Lyndsey Wajert (BUSL '19) and Danielle Deluty (BUSL '19) are working on the next phase of the case.

Dr. Johnson received funding to file this suit from the National Freedom of Information Coalition, through a grant from the John S. and James L. Knight Foundation.

The opinion is Johnson v. CIA, --- F. Supp. 3d ---, 2018 WL 4440541 (D. Mass. Sept. 17, 2018). You can read some of the documents in the case here:

Additional reading: